AI Glossary: Letter "F"

The F1 Score is a statistical metric used to evaluate a classification model's accuracy. It is calculated as the harmonic mean of precision (exactness) and recall (completeness), making it ideal for datasets with imbalanced classes.

A Feature is an individual, measurable property or input variable used by a machine learning model to make predictions. In tabular datasets, features correspond to columns (e.g. square footage, age of home).

Feature Engineering is the process of using domain knowledge to select, transform, combine, and manipulate raw variables into highly predictive input features for machine learning algorithms.

Federated Learning is a decentralized training technique that trains machine learning models across multiple remote edge devices holding local data samples, without exchanging the data itself.

Few-Shot Learning is a machine learning paradigm where a model is trained or prompted to perform a task using only a small number of training examples. In LLMs, this is achieved by including a few demonstration inputs and outputs directly in the prompt context window.

Fine-Tuning is the process of taking a pre-trained model and training it further on a smaller, specific dataset to adapt it for a particular task or domain. Fine-tuning alters the internal weights of the network, specializing its behavior and tone.

FlashAttention is a memory-efficient, exact self-attention algorithm that speeds up Transformer training and inference by tiling computations in GPU SRAM and avoiding HBM access.

A Foundation Model is a large-scale AI model trained on massive, broad datasets (typically through self-supervised learning) that serves as the baseline starting point for multiple downstream tasks. Examples include GPT-4, LLaMA, and stable diffusion models.

A Fully Connected Layer (Dense Layer) is a layer in an artificial neural network where every neuron is connected to all neurons in the previous layer, mapping linear combinations of inputs to outputs.

Function Calling is an LLM capability where the model outputs a structured JSON object containing argument parameters to invoke specific external functions or APIs, enabling LLMs to act as dynamic interfaces for databases and systems.